Optimization of a PM2.5 automatic monitoring network based on the modeling approach – A case study in Long An province, Vietnam

Abstract

Abstract Currently, fine particulate matter (PM2.5) pollution is one of the well-recognized serious environmental health risks. In particular, PM2.5 has remarkably impacted the human respiratory, cardiovascular, and circulatory systems. Vietnam has frequently ranked among the countries with the highest annual mean PM2.5 levels in Southeast Asia and worldwide. Thus, building a real-time continuous ambient PM2.5 monitoring network is an important environmental management tool to reduce health effects attributable to PM2.5 exposure. Nevertheless, measurement locations of an air quality monitoring (AQM) network depend strongly on meteorological conditions in the area and sensitivity to local primary emission sources, which significantly determines the AQM network’s operational efficiency. This study aims to estimate and optimize outdoor PM2.5 monitoring sites of an automatic air quality monitoring (AAQM) network using a system of low-cost sensors in Long An province, based on the spatio-temporal distribution assessment results of PM2.5 concentrations from WRF (Weather Research and Forecasting Model)/CMAQ (the Community Multiscale Air Quality) models. The prominent study outcomes have shown that the 2018 average PM2.5 concentration in Long An province exceeded the threshold of QCVN 05:2023/BTNMT (25 μg/m3) from 5.6 to 7.9 times. Moreover, this study has proposed four optimal monitoring sites (AQ1, AQ2, AQ3, and AQ4) for the automatically real-time PM2.5 observation in the areas of Tan An City, Can Giuoc District, the ring roads 3 and 4, and Kien Tuong Town. The study has provided an important scientific basis and support for local air quality management activities; furthermore, the newly proposed monitoring locations will complement the province’s periodic monitoring program towards 2025.